Multi-media context language processing

ABSTRACT

Technology is disclosed that improves language processing engines by using multi-media (image, video, etc.) context data when training and applying language models. Multi-media context data can be obtained from one or more sources such as object/location/person identification in the multi-media, multi-media characteristics, labels or characteristics provided by an author of the multi-media, or information about the author of the multi-media. This context data can be used as additional input for a machine learning process that creates a model used in language processing. The resulting model can be used as part of various language processing engines such as a translation engine, correction engine, tagging engine, etc., by taking multi-media context/labeling for a content item as part of the input for computing results of the model.

BACKGROUND

The Internet has made it possible for people to connect and shareinformation across the globe in ways previously undreamt of. Socialmedia platforms, for example, enable people on opposite sides of theworld to collaborate on ideas, discuss current events, or just sharewhat they had for lunch. In the past, this spectacular resource has beensomewhat limited to communications between users having a common naturallanguage (“language”). Users have only been able to consume content thatis in their language, or for which a content provider is able todetermine an appropriate translation. Furthermore, the accuracy oflanguage processing has been limited because machines have been unableto appropriately determine and apply contextual information forprocessing language.

Although language processing is a particular challenge, several types oflanguage processing engines, such as parts-of-speech tagging engines,correction engines, and machine translation engines, have been createdto address this concern. These language processing engines enable“content items,” which can be any item containing natural languageincluding text, images, audio, video, or other multi-media, to bequickly classified, translated, sorted, read aloud, tagged, andotherwise used by machines. However, content items can be inaccuratelyprocessed due to rules and engines that do not account for the contextof content items. For example, the word “lift” can mean “move upward”among speakers of American English (as that word is commonly used inAmerica), whereas it can mean “elevator” for British English speakers. Acontent item including the phrase, “press the button for the lift,”could be translated into either “press the button for the elevator” or“press the button to go up.” In addition, machine translations of acontent item are often based on dictionary translations and do notconsider context, which often makes a significant difference such as inslang or colloquial passages.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an overview of devices on whichsome implementations can operate.

FIG. 2 is a block diagram illustrating an overview of an environment inwhich some implementations can operate.

FIG. 3 is a block diagram illustrating components which, in someimplementations, can be used in a system employing the disclosedtechnology.

FIG. 4 is a flow diagram illustrating a process used in someimplementations for training a model using a multi-media context.

FIG. 5 is a flow diagram illustrating a process used in someimplementations for applying a model to a content item with amulti-media context.

The techniques introduced here may be better understood by referring tothe following Detailed Description in conjunction with the accompanyingdrawings, in which like reference numerals indicate identical orfunctionally similar elements.

DETAILED DESCRIPTION

Embodiments for identifying and using a multi-media context duringlanguage processing are described. Multi-media context data can beobtained from one or more sources such as object, location, or personidentification in multi-media items, multi-media characteristics, labelsor characteristics provided by an author of the multi-media, orinformation about the author of the multi-media. This context data canbe used as part of the input for a machine learning process that createsa model used in language processing. The resulting model can takemulti-media context data or labeling for a content item as part of theinput for computing results of the model. These models can be used aspart of various language processing engines such as a translationengine, correction engine, tagging engine, etc.

“Multi-media,” as used herein, refers to one or more of: an image, avideo, a sound file, a webpage, hyperlink, an application, a widget, ascript, or any combination thereof. As used herein, a “snippet” or“n-gram” is a digital representation of one or more words or groups ofcharacters from a natural language. In some implementations, snippetscan be obtained from social network content items, such as posts. A“model,” as used herein, refers to a construct that is trained usingtraining data to make predictions or to provide probabilities for newdata items, whether or not the new data items were included in thetraining data. For example, training data can include items with variousparameters and an assigned classification. A new data item can haveparameters that a model can use to assign a classification to the newdata item. As another example, a model can be a probability distributionresulting from the analysis of training data, such as the likelihood ofan n-gram occurring in a given language based on an analysis of a largecorpus from that language, the likelihood of an n-gram occurring in atranslation given an input, or the likelihood of an n-gram occurring ina translation given an input and other parameters. Examples of modelsinclude: neural networks, support vector machines, decision trees,Parzen windows, Bayes, clustering, reinforcement learning, probabilitydistributions, and others. Models can be configured for varioussituations, data types, sources, and output formats.

When processing a content item that includes language, the contextprovided by multi-media associated with the content item can affect howthat language should be processed. For example, if a content itemincluded the phrase “How do you like my rog?” a language processingengine configured to make corrections may produce candidate correctionsincluding: “How do you like my dog?” “How do you like my hog?” and “Howdo you like my jog?” The content item can be associated with a picture,and an object analysis of the picture can determine that a dog isdepicted in the picture. This multi-media context can be used by alanguage model that is part of the correction engine to select the “Howdo you like my dog?” correction.

In some implementations, a multi-media context system can train modelswith training data that comprises a language corpus with variousportions of the language corpus associated with various multi-medialabels. In some implementations, training data can comprise multiplepairs of (A) starting snippets (e.g. snippets to be translated orsnippets to be corrected) and (B) output language (e.g. translatedsnippets or corrected snippets) where each pair is associated with oneor more multi-media items or labels. The multi-media context system canidentify labels for the multi-media items and use these labels as partof input to the model when performing model training. These labels canpartially control the output from the model which the multi-mediacontext system can compare to the corresponding output language toadjust parts of the model, thereby training it. Additional detailsdescribing training a language processing model to use multi-mediacontext data are discussed below in relation to FIG. 4.

The multi-media context system can apply trained models in languageprocessing operations on content items that are associated with one ormore multi-media items. For example, a content item can be a post to asocial media website and the post can contain a link to a video. Themulti-media context system can perform an analysis of the associatedmulti-media items to obtain labels for the multi-media items. Forexample, the multi-media context system can apply an objectidentification algorithm to the video that is linked to the post contentitem and use the resulting object identifications as labels from thevideo. The multi-media context system can then apply a trained model toa combination of: a representation of language in the content item andrepresentations of the labels. Results of the trained model can be usedfor processing the language in the content item. Additional detailsdescribing applying a language processing model with multi-media contextdata are discussed below in relation to FIG. 5.

Attempts to develop language processing engines in fields such astranslation, error correction, parts-of-speech tagging, etc., have beentried since people have been creating digital representations oflanguage. These attempts have developed numerous sophisticatedalgorithms employing various technical mechanisms such as distributedcomputing, machine learning, targeted programming, etc. Languageprocessing engines can be improved by employing multi-media contextdata. For example, to understand many content items that employlanguage, a multi-media context of the content item may be helpful oreven necessary. Therefore, systems that utilize multi-media context datawhen performing language processing can improve the language processingfield.

Several implementations are discussed below in more detail in referenceto the figures. Turning now to the figures, FIG. 1 is a block diagramillustrating an overview of devices on which some implementations of thedisclosed technology can operate. The devices can comprise hardwarecomponents of a device 100 that implements multi-media context languageprocessing. Device 100 can include one or more input devices 120 thatprovide input to the CPU (processor) 110, notifying it of actions. Theactions are typically mediated by a hardware controller that interpretsthe signals received from the input device and communicates theinformation to the CPU 110 using a communication protocol. Input devices120 include, for example, a mouse, a keyboard, a touchscreen, aninfrared sensor, a touchpad, a wearable input device, a camera- orimage-based input device, a microphone, or other user input devices.

CPU 110 can be a single processing unit or multiple processing units ina device or distributed across multiple devices. CPU 110 can be coupledto other hardware devices, for example, with the use of a bus, such as aPCI bus or SCSI bus. The CPU 110 can communicate with a hardwarecontroller for devices, such as for a display 130. Display 130 can beused to display text and graphics. In some examples, display 130provides graphical and textual visual feedback to a user. In someimplementations, display 130 includes the input device as part of thedisplay, such as when the input device is a touchscreen or is equippedwith an eye direction monitoring system. In some implementations, thedisplay is separate from the input device. Examples of display devicesare: an LCD display screen, an LED display screen, a projected,holographic, or augmented reality display (such as a heads-up displaydevice or a head-mounted device), and so on. Other I/O devices 140 canalso be coupled to the processor, such as a network card, video card,audio card, USB, firewire or other external device, camera, printer,speakers, CD-ROM drive, DVD drive, disk drive, or Blu-Ray device.

In some implementations, the device 100 also includes a communicationdevice capable of communicating wirelessly or wire-based with a networknode. The communication device can communicate with another device or aserver through a network using, for example, TCP/IP protocols. Device100 can utilize the communication device to distribute operations acrossmultiple network devices.

The CPU 110 can have access to a memory 150. A memory includes one ormore of various hardware devices for volatile and non-volatile storage,and can include both read-only and writable memory. For example, amemory can comprise random access memory (RAM), CPU registers, read-onlymemory (ROM), and writable non-volatile memory, such as flash memory,hard drives, floppy disks, CDs, DVDs, magnetic storage devices, tapedrives, device buffers, and so forth. A memory is not a propagatingsignal divorced from underlying hardware; a memory is thusnon-transitory. Memory 150 can include program memory 160 that storesprograms and software, such as an operating system 162, multi-mediacontext system 164, and other application programs 166. Memory 150 canalso include data memory 170 that can include content items, multi-mediaitems, labels, content author characteristics, content interactions,models, configuration data, settings, user options or preferences, etc.,which can be provided to the program memory 160 or any element of thedevice 100.

Some implementations can be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that may be suitable for use with the technologyinclude, but are not limited to, personal computers, server computers,handheld or laptop devices, cellular telephones, wearable electronics,tablet devices, multiprocessor systems, microprocessor-based systems,set-top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, distributed computing environmentsthat include any of the above systems or devices, or the like.

FIG. 2 is a block diagram illustrating an overview of an environment 200in which some implementations of the disclosed technology can operate.Environment 200 can include one or more client computing devices 205A-D,examples of which can include device 100. Client computing devices 205can operate in a networked environment using logical connections 210through network 230 to one or more remote computers, such as a servercomputing device.

In some implementations, server 210 can be an edge server which receivesclient requests and coordinates fulfillment of those requests throughother servers, such as servers 220A-C. Server computing devices 210 and220 can comprise computing systems, such as device 100. Though eachserver computing device 210 and 220 is displayed logically as a singleserver, server computing devices can each be a distributed computingenvironment encompassing multiple computing devices located at the sameor at geographically disparate physical locations. In someimplementations, each server 220 corresponds to a group of servers.

Client computing devices 205 and server computing devices 210 and 220can each act as a server or client to other server/client devices.Server 210 can connect to a database 215. Servers 220A-C can eachconnect to a corresponding database 225A-C. As discussed above, eachserver 220 can correspond to a group of servers, and each of theseservers can share a database or can have their own database. Thoughdatabases 215 and 225 are displayed logically as single units, databases215 and 225 can each be a distributed computing environment encompassingmultiple computing devices, can be located within their correspondingserver, or can be located at the same or at geographically disparatephysical locations.

Network 230 can be a local area network (LAN) or a wide area network(WAN), but can also be other wired or wireless networks. Network 230 maybe the Internet or some other public or private network. Clientcomputing devices 205 can be connected to network 230 through a networkinterface, such as by wired or wireless communication. While theconnections between server 210 and servers 220 are shown as separateconnections, these connections can be any kind of local, wide area,wired, or wireless network, including network 230 or a separate publicor private network.

FIG. 3 is a block diagram illustrating components 300 which, in someimplementations, can be used in a system employing the disclosedtechnology. The components 300 include hardware 302, general software320, and specialized components 340. As discussed above, a systemimplementing the disclosed technology can use various hardware includingcentral processing units 304, working memory 306, storage memory 308,and input and output devices 310. Components 300 can be implemented in aclient computing device such as client computing devices 205 or on aserver computing device, such as server computing device 210 or 220.

General software 320 can include various applications including anoperating system 322, local programs 324, and a basic input outputsystem (BIOS) 326. Specialized components 340 can be subcomponents of ageneral software application 320, such as local programs 324.Specialized components 340 can include multi-media labeler 344, languageprocessing model trainer 346, language processing models 348, andcomponents which can be used for transferring data and controlling thespecialized components, such as interface 342. In some implementations,components 300 can be in a computing system that is distributed acrossmultiple computing devices or can include an interface to a server-basedapplication.

Multi-media labeler 344 can be configured to receive a multi-media item,such as through interface 342. Multi-media labeler 344 can analyze thereceived multi-media item to determine labels. In some implementations,these labels can include identification of objects, identification ofpeople, or identification of places. In some implementations,multi-media labeler 344 can retrieve these labels from assignations madeby an author of a content item associated with the multi-media item. Forexample, a user posting a content item with multi-media to a socialmedia website can provide labels such as tags of people in a photo, oneor more pre-defined tags corresponding to moods or actions describingthe multi-media, or textual description of the multi-media item. In someimplementations, multi-media labeler 344 can retrieve these labels fromcharacteristics of an author of a content item associated with amulti-media item. In some implementations, multi-media labeler 344 canretrieve these labels from interactions of users with a content itemassociated with a multi-media item or from characteristics of users whoperform such interactions.

Language processing model trainer 346 can be configured to generate amodel, of models 348, that can be used in various language processingengines. For example, language processing model trainer 346 can generatelanguage models, translation models, correction models, tagging models,etc. Language processing model trainer 346 can train a model byreceiving training data content items that are each associated with oneor more multi-media items and that have a desired output from a model.Language processing model trainer 346 can employ multi-media labeler 344to determine labels for the multi-media items. These labels for amulti-media item can be combined with a representation of the languagefrom the content item corresponding to the multi-media item, for examplein a sparse vector or embedding. Each of these combinations can be usedas input to a model. Based on a comparison of the output of the modelfor each content item and the desired output corresponding to eachcontent item, parameters of the model can be trained. After applying andmodifying the model for the items in the training data, the model can betrained to operate on new content items that have correspondingmulti-media items.

Language processing models 348 can be generated by language processingmodel trainer 346. Particular ones of language processing models 348 canbe applied to content items that are associated with one or moremulti-media items, whether or not the content items were in trainingdata used to create the language processing model. Applying one oflanguage processing models 348 to a content item associated with one ormore multi-media items can include employing multi-media labeler 344 todetermine labels for the multi-media items. These labels for amulti-media item can be combined with a representation of the languagefrom the content item corresponding to the multi-media item. Thecombination can be used as input to the language processing model, andthe result can inform the processing of a language processing engine.

Those skilled in the art will appreciate that the components illustratedin FIGS. 1-3 described above, and in each of the flow diagrams discussedbelow, may be altered in a variety of ways. For example, the order ofthe logic may be rearranged, substeps may be performed in parallel,illustrated logic may be omitted, other logic may be included, etc.

FIG. 4 is a flow diagram illustrating a process 400 used in someimplementations for training a model using a multi-media context.Process 400 begins at block 402 and continues to block 403. At block403, process 400 can obtain a model to train. The obtained model can bevarious types of probability distributions or machine learning objectssuch as a neural network, support vector machine, Bayesian model,decision tree, etc. This model can initially have default or randomparameters set. For example, where the model is a neural network, theweights between nodes can be set to default or random values. At block404, process 400 can obtain training data that includes multiple contentitems, each associated with one or more multi-media items. In someimplementations, content items can be from a social media website suchas wall posts, news feeds, messages, group pages, event pages, etc.

At block 406, process 400 can set a first content item of the obtainedtraining data as a selected content item to be operated on by the loopbetween blocks 408 and 414. At block 408, process 400 can identifylabels for the multi-media items. In some implementations, identifyinglabels comprises performing an object recognition analysis on themulti-media items. In some implementations, the object recognitionanalysis can identify objects from a defined set of objects. In someimplementations, identifying labels comprises identifying people in themulti-media items. Identifying people can include, e.g., facialrecognition algorithms. In some implementations, identifying labelscomprises place recognition, e.g., by recognizing landmarks, associatedGPS or other location data, etc. In some implementations, identifyinglabels comprises extracting meta-data from the multi-media items, e.g.,timestamps, location markers, source id, etc. In some implementations,identifying labels comprises identifying words in the multi-media, e.g.,with speech recognition on sounds or with text recognition on images.

In some implementations, identifying labels comprises obtaining labelsprovided by an author of the content item or by users of a social mediasystem. For example, a content item author can provide a labelindicating what the author is feeling, writing about, watching,celebrating, thinking about, listening to, looking for, traveling to,playing, making, attending, reading, getting, eating, drinking, meeting,or that they are exercising. In some implementations, identifying labelscomprises information about the multi-media item such as its file size,dimensions, colors used or color profiles, whether it is a manufacturedimage (e.g. a drawing or a computer created image) or captured by animage capture device (e.g. a photo), a date, length, type, encoding,etc. In some implementations, identifying labels comprises identifyingcharacteristics of an author of the content item, such as gender, age,location, educational level, friends, profession, relationship status,etc. In some implementations, identifying labels comprises identifyingstatistics and characteristics of users who access the content item. Forexample, labels can include a number of accesses, times of the accesses,a user rating, number of shares, an identified type of typical users whoview the content item, etc. In some implementations, the labelsidentified at block 408 can be from a defined set of possible labels.

Process 400 can assign the labels identified at block 408 to content(e.g. n-grams) of the content item associated with the multi-media itemsfor which the labels were generated. In some implementations, the labelscan be assigned to parts of the content items. For example, where alabel is identified as corresponding to a particular type of speech(e.g. a noun) one or more identified noun type labels can be associatedwith nouns or pronouns in the content item. The same can be done forother parts of speech, such as where a user defined label identified anaction which can be assigned to a verb or adverb in the content item. Asa more specific example, where a content item includes the phrase “Thatis great!” which is associated with a picture of a car, a labelcorresponding to the noun car can be associated with the pronoun “that”in the content item. The determination of types of speech can use POStagging technology, such as is described in U.S. patent application Ser.No. 14/804,802, entitled “DATA SORTING FOR LANGUAGE PROCESSING SUCH ASPOS TAGGING,” incorporated herein by reference.

At block 410, process 400 can enrich the selected content item byassociating with it the labels identified at block 408. In someimplementations, the training data can comprise pairs of (A) model inputwith associated multi-media context data and (B) exemplary model outputdata. In some implementations, the model input can be an embedding ofthe phrase combined with the multi-media context data labeling. In someimplementations, the training data can comprise a corpus of phraseswhere at least some of the phrases are associated with multi-mediacontext data.

At block 412, process 400 can determine whether all the content itemsobtained at block 404 have been operated on by the loop between blocks408-414, and if so process 400 continues to block 416. If not, process400 continues to block 414, where a next item of the content items ofthe training data obtained at block 404 is set as the selected contentitem to be operated on by the loop between blocks 408-414.

At block 416, process 400 can use the training data, enhanced with thelabels by the loop between blocks 408-414, to train the model receivedat block 403. In some implementations, the model can be a language modelindicating a probability distribution showing the likelihood of phrasesoccurring given a multi-media context. In some implementations, themodel can be a translation model indicating a probability distributionshowing the likelihood of phrases occurring in a translation given amulti-media context. In some implementations, the model can be atranslation model indicating a likely translation of a source phrasegiven the source phrase and a multi-media context. The model can beother types of models such as correction models or parts-of-speechtagging models. Training a language model can include examining thetraining data and assigning, to each n-gram in the training data, aprobability based on its comparative frequency of occurrence in thetraining data. This probability can be further based on the labelsassigned at block 408 to n-grams in the training data, such that theprobabilities are probabilities given a set of labels.

Training a model that relies on a neural network can includeencapsulating the labels and/or n-grams of the content item in a formatthat can be fed into the neural network, such as in a vector. Someembodiments for encapsulating n-grams in a vector are described in U.S.patent application Ser. No. 14/878,794, entitled “LANGUAGE INDEPENDENTREPRESENTATIONS,” incorporated herein by reference. A neural network canbe trained with supervised learning, such as for translations, where thetraining data includes the content item and associated labels as inputand a desired output, such as a known translation of the content item. Arepresentation of each content item with associated representations oflabels determined at block 408 can be provided to the neural networkmodel received at block 403. Output from the model can be compared tothe desired output for that content item and, based on the comparison,the neural network can be modified, such as by changing weights betweennodes of the neural network or parameters of the functions used at eachnode in the neural network. After applying each of the content items inthe training data and modifying the neural network in this manner, theneural network model can be trained to evaluate new content items withassociated multi-media, as discussed below in relation to FIG. 5. Atblock 418, process 400 can return the model trained at block 416.Process 400 can then continue to block 420, where it ends.

FIG. 5 is a flow diagram illustrating a process 500 used in someimplementations for applying a model to a content item with amulti-media context. Process 500 begins at block 502 and continues toblock 504. At block 504, process 500 can receive a content itemassociated with one or more multi-media items. As discussed above,multi-media items can be pictures, video, sound, web links, apps,scripts, etc.

At block 506, process 500 can obtain a language processing model. Insome implementations, the language processing model obtained at block506 can be a language processing model trained using process 400.

At block 508, process 500 can evaluate the multi-media items associatedwith the content item received at block 504. This evaluation canidentify one or more labels for the multi-media item. Representations ofthese labels can be associated with the entire content item or withportions of the content item received at block 504. In someimplementations, evaluation of multi-media items can be performed in thesame manner by process 500 as is performed by process 400 at block 408.Evaluation of multi-media items to determine labels can be the sameprocess used to label training data that was used to create the modelreceived at block 506. In various implementations, these representationscan be tags or vector encapsulations of the labels associated withvarious content items or portions (e.g. n-grams) of content items.

At block 510, process 500 can apply the language processing modelreceived at block 506 to a combination of a representation of thecontent item received at block 504 and representations of the labelsdetermined at block 508. In some implementations, process 500 can applya language model which can include determining which of several possiblephrases is most likely given the multi-media labels determined at block508. For example, where the language processing performed by process 500is translations, a translation model can produce multiple possibletranslations of a content item and the content item can have beenassigned labels corresponding to an associated multi-media item. In thisexample, process 500 can have received a language processing model atblock 506 that gives phrase probabilities given multi-media labels.Applying this model at block 510, process 500 can obtain a likelihood ofeach of the possible translations given the associated labels. Thepossible translation with the highest likelihood can be selected as apreferred translation.

As another example, the language processing performed by process 500 canbe parts-of-speech tagging, and a content item to be tagged has beenassigned labels corresponding to an associated multi-media item at block508. The model received at block 506 can be a neural network trained toperform tagging where the input to the model includes multi-medialabels. One or more words from the content item can be provided to thetagging model, together with a representation of corresponding labels,and the model can provide a tag for that portion of the content item.

At block 512, process 500 can return results from applying the languageprocessing model at block 510. Process 500 can then proceed to block514, where it ends.

Several implementations of the disclosed technology are described abovein reference to the figures. The computing devices on which thedescribed technology may be implemented can include one or more centralprocessing units, memory, input devices (e.g., keyboard and pointingdevices), output devices (e.g., display devices), storage devices (e.g.,disk drives), and network devices (e.g., network interfaces). The memoryand storage devices are computer-readable storage media that can storeinstructions that implement at least portions of the describedtechnology. In addition, the data structures and message structures canbe stored or transmitted via a data transmission medium, such as asignal on a communications link. Various communications links can beused, such as the Internet, a local area network, a wide area network,or a point-to-point dial-up connection. Thus, computer-readable mediacan comprise computer-readable storage media (e.g., “non-transitory”media) and computer-readable transmission media.

As used herein, being above a threshold means that a value for an itemunder comparison is above a specified other value, that an item undercomparison is among a certain specified number of items with the largestvalue, or that an item under comparison has a value within a specifiedtop percentage value. As used herein, being below a threshold means thata value for an item under comparison is below a specified other value,that an item under comparison is among a certain specified number ofitems with the smallest value, or that an item under comparison has avalue within a specified bottom percentage value. As used herein, beingwithin a threshold means that a value for an item under comparison isbetween two specified other values, that an item under comparison isamong a middle specified number of items, or that an item undercomparison has a value within a middle specified percentage range.

As used herein, the word “or” refers to any possible permutation of aset of items. For example, the phrase “A, B, or C” refers to at leastone of A, B, C, or any combination thereof, such as any of: A; B; C; Aand B; A and C; B and C; A, B, and C; or multiple of any item such as Aand A; B, B, and C; A, A, B, C, and C; etc.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Specific embodiments and implementations have been described herein forpurposes of illustration, but various modifications can be made withoutdeviating from the scope of the embodiments and implementations. Thespecific features and acts described above are disclosed as exampleforms of implementing the claims that follow. Accordingly, theembodiments and implementations are not limited except as by theappended claims.

Any patents, patent applications, and other references noted above areincorporated herein by reference. Aspects can be modified, if necessary,to employ the systems, functions, and concepts of the various referencesdescribed above to provide yet further implementations. If statements orsubject matter in a document incorporated by reference conflicts withstatements or subject matter of this application, then this applicationshall control.

1. A method for generating a language processing model, comprising:obtaining multiple content items, wherein each content item isassociated with one or more multi-media items and comprises one or moren-grams, wherein an n-gram is a digital representation of one or morewords or groups of characters; for each selected content item of themultiple content items: identifying one or more multi-media labels forthe one or more multi-media items associated with the selected contentitem; assigning to the identified one or more multi-media labels atleast some of the one or more n-grams of the selected content item; andincluding, in a language corpus, the one or more n-grams of the selectedcontent item; and generating the language processing model comprising aprobability distribution by computing, for each selected n-gram ofmultiple n-grams in the language corpus, a frequency that the selectedn-gram occurs in the language corpus, wherein the probabilitydistribution can take representations of multi-media labels asparameters, and wherein at least some probabilities provided by theprobability distribution are multi-media context probabilitiesindicating a probability of a chosen n-gram occurring, given that thechosen n-gram is associated with provided multi-media labels, whereinthe multi-media context probabilities are based on the assigning of theidentified one or more multi-media labels.
 2. The method of claim 1,wherein the language processing model is a translation model, andwherein the multi-media context probabilities indicate a probability ofan output n-gram being in a translation of an input n-gram, given: (A)the input n-gram and (B) multi-media labels associated with the inputn-gram.
 3. The method of claim 1, wherein identifying the one or moremulti-media labels for the one or more multi-media items associated withthe selected content item comprises performing object identification onthe one or more multi-media items.
 4. The method of claim 1, whereinidentifying the one or more multi-media labels for the one or moremulti-media items associated with the selected content item comprisesperforming object identification on the one or more multi-media items;and wherein the one or more multi-media labels are selected from apre-defined set of labels comprising at least object labels and/or placelabels.
 5. The method of claim 1, wherein identifying the one or moremulti-media labels for the one or more multi-media items associated withthe selected content item comprises obtaining labels that wereassociated with the content item by an author of the content item. 6.The method of claim 1, wherein identifying the one or more multi-medialabels for the one or more multi-media items associated with theselected content item comprises obtaining labels based on one or moreof: characteristics of an author of the content item, characteristics ofuser interactions with the content item, characteristics of users whohave interacted with the content item, or any combination thereof. 7.The method of claim 1, wherein the one or more multi-media items that atleast one of the content items is associated with comprises an image orvideo; and wherein identifying the one or more multi-media labels forthe image comprises determining for the image one or more of: a filesize; dimensions of the image or video; colors used in the image orvideo; a color profile for the image or video; whether or not the imageor video was captured by an image capture device; a date the image orvideo was created; a type corresponding to the image or video; anencoding of the image or video; or any combination thereof.
 8. Themethod of claim 1, wherein the one or more multi-media items that atleast one of the content items is associated with comprises an image. 9.The method of claim 1, wherein the one or more multi-media items that atleast one of the content items is associated with comprises one or moreof: a video, a file including sound, an application, or any combinationthereof.
 10. The method of claim 1, wherein assigning the identified oneor more multi-media labels to at least some of the one or more n-gramsof the selected content item comprises: determining a label typeassociated with a selected label of the one or more labels; determiningan n-gram type associated with a particular n-gram of the one or moren-grams; matching the label type to the n-gram type; and based on thematching, assigning the selected label to the particular n-gram.
 11. Anon-transitory computer-readable storage medium storing instructionsthat, when executed by a computing system, cause the computing system toperform operations for applying a translation model, the operationscomprising: obtaining a content item associated with one or moremulti-media items and comprising one or more input n-grams, wherein ann-gram is a digital representation of one or more words or groups ofcharacters; identifying one or more multi-media labels for the one ormore multi-media items associated with the content item; assigning tothe identified one or more multi-media labels at least some of the oneor more input n-grams of the content item; obtaining the translationmodel, wherein the translation model comprises a probabilitydistribution indicating, for selected n-grams, a probability that anoutput n-gram is a translation of the selected n-gram, given one or moremulti-media labels; and applying, to the content item, the obtainedtranslation model by selecting one or more output n-grams that, based onthe probability distribution, have a highest probability of being thetranslation of the at least some of the one or more input n-grams of thecontent item, given the identified one or more multi-media labelsassigned to the at least some of the one or more input n-grams of thecontent item.
 12. The non-transitory computer-readable storage medium ofclaim 11, wherein identifying the one or more multi-media labels for theone or more multi-media items associated with the selected content itemcomprises identifying, in at least one of the one or more multi-mediaitems, at least one of: an object, a place, a person, or any combinationthereof.
 13. The non-transitory computer-readable storage medium ofclaim 11, wherein identifying the one or more multi-media labels for theone or more multi-media items associated with the selected content itemcomprises identifying, in at least one of the one or more multi-mediaitems, at least one of: an object, a place, a person, or any combinationthereof; and wherein the one or more multi-media labels are selectedfrom a set of pre-defined labels.
 14. The non-transitorycomputer-readable storage medium of claim 11, wherein identifying theone or more multi-media labels for the one or more multi-media itemsassociated with the selected content item comprises obtaining labelsthat were associated with the content item by an author of the contentitem.
 15. The non-transitory computer-readable storage medium of claim11, wherein identifying the one or more multi-media labels for the oneor more multi-media items associated with the selected content itemcomprises obtaining labels based on one or more of: characteristics ofan author of the content item, characteristics of user interactions withthe content item, characteristics of users who have interacted with thecontent item, or any combination thereof.
 16. The non-transitorycomputer-readable storage medium of claim 11, wherein the one or moremulti-media items that at least one of the content items is associatedwith comprises an image or video; and wherein identifying the one ormore multi-media labels for the image comprises determining one or moreof: a file size of the image or video; dimensions of the image or video;colors used in the image or video; a color profile for the image orvideo; whether or not the image or video was captured by an imagecapture device; a date the image or video was created; a typecorresponding to the image or video; an encoding of the image or video;or any combination thereof.
 17. The non-transitory computer-readablestorage medium of claim 11, wherein the one or more multi-media itemsthat at least one of the content items is associated with comprises oneor more of: an image, a video, a file including sound, or anycombination thereof.
 18. A system for generating a model, comprising:one or more processors; a memory; an interface configured to obtainmultiple content items, wherein each of the multiple content items isassociated with one or more multi-media items and comprises one or moren-grams, wherein an n-gram is a digital representation of one or morewords or groups of characters; a multi-media labeler configured to, foreach selected content item of the multiple content items: identify oneor more multi-media labels for the one or more multi-media itemsassociated with the selected content item; assign to the identified oneor more multi-media labels at least some of the one or more n-grams ofthe selected content item; and include, in a language corpus, the one ormore n-grams of the selected content item; and a language processingmodel trainer configured to generate the model by computing aprobability distribution that indicates, for each selected n-gram ofmultiple n-grams in the language corpus, a frequency that the selectedn-gram occurs in the language corpus, wherein the probabilitydistribution can take representations of multi-media labels asparameters, and wherein at least some probabilities provided by theprobability distribution are multi-media context probabilitiesindicating a probability of a chosen n-gram occurring, given that thechosen n-gram is associated with provided multi-media labels, whereinthe multi-media context probabilities are based on the assigning of theidentified one or more multi-media labels.
 19. The system of claim 18,wherein the multi-media labeler is configured to identify the one ormore multi-media labels for the one or more multi-media items associatedwith the selected content item by identifying, in at least one of theone or more multi-media items, at least one of: an object, a place, aperson, or any combination thereof; and wherein the one or moremulti-media labels are selected from a set of pre-defined labels. 20.The system of claim 18, wherein the multi-media labeler is configured toassign the identified one or more multi-media labels to at least some ofthe one or more n-grams of the selected content item by: determining alabel type associated with a selected label of the one or more labels;determining an n-gram type associated with a particular n-gram of theone or more n-grams; matching the label type to the n-gram type; andbased on the matching, assigning the selected label to the particularn-gram.